Wolbarg: Why I swapped Postgres for SQLite for AI Memory

My team spent way too long over-engineering our agent memory layer, and I'll be the first to admit I fell into the "Postgres by default" trap. We were building out our internal AI workflow tools and the instinct was to go with a heavy-duty database because we assumed the concurrent read/writes from multiple agents would crush anything smaller. I spent a week fighting with container orchestration and connection pooling just to keep the memory state synced across our local dev environments.

Then we started testing Wolbarg, this local-first shared memory SDK, and the benchmarks completely flipped my perspective.

For anyone doing single-node deployments or local-first agent setups, SQLite is actually a beast. We ran realistic agent workloads—lots of rapid-fire state updates and retrieval—and SQLite didn't just keep up; it simplified our entire deployment pipeline. We went from managing a separate DB instance to just handling a file.

That said, it's not a magic bullet. If you're scaling across a massive cluster or need high-availability failover, Postgres is still the king. But for the vast majority of LLM agent use cases where the "memory" is tied to a specific session or local environment, the overhead of a full server is just waste.

If you're trying to build a shared memory system from scratch, don't default to the biggest tool in the shed. The latency gains from keeping the data local are real, and the reduction in architectural complexity is the biggest win for our team's velocity.